Diagnostic performance of large language models on the NEJM image challenge: a comparative study with human evaluators and the impact of prompt engineering

Within the context of this standardized challenge, a leading multimodal LLM delivered high diagnostic accuracy that surpassed the scores of both peer models and the recruited human participants. However, these results should be interpreted as evidence of pattern recognition capabilities rather than human-like clinical understanding. While further validation on real-world data is warranted, these findings support the potential utility of LLMs in educational and standardized settings, highlighting that most residual errors are due to logic gaps that can be partly mitigated by refined user prompting, emphasizing the importance of human-AI interaction for maximizing reliability.